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Obesity: an American Crisis

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Obesity: An American Crisis
EC315 (Term Project Report)
Michael Higgs
Fall 2 2012

TABLE OF CONTENTS

Purpose Statement…………………………………………………Page 1
Definition of Variables……………………………………………Page 1-3
Data Description…………………………………………………….Page 3-4
Results……………………………………………………………………Page 4-6
Works Cited……………………………………………………………Page 7
Appendix……………………………………………………………….Page 8-10

Purpose Statement

This project will discuss the correlation between exercise and diet on the ever growing crisis of obesity in America. The dependent variable obesity is determined by independent variables; exercise (lack of) and diet. Americans are not getting adequate exercise to burn their caloric intake. This is not only a concern for adults, the CDC (center for disease control) reports that “most adolescents fall shorts of the recommended 60 minutes of aerobic physical activity each day, as only 18% of students in grades 9-12 met this recommendation in 2007.” Instead of exercising outdoors, children are turning to video games and television to occupy their time. Nicolas Stettler, MD performed a study that suggested that every hour children play video games or watch television may double their risk of obesity. With this being said I feel the most important independent variable in this relationship is exercise, or the lack of it. By teaching proper exercise and nutrition at a young age the likelihood of being obese as an adult diminishes.

Definition of Variables

The model used is: Y = X1 + X2
Where:
Y = Obesity
X1 = Exercise (lack of)
X2 = Diet

Y: The result or dependent variable is obesity. It is defined by the percentage of overweight people, children and adults in America. Data taken from the CDC (Center for disease control).

X1: The primary independent variable is exercise (lack of). The CDC reports that less than half (48%) of all adults meet the 2008 physical activity guidelines. Less than 30% of high school students get at least 60 minutes of physical activity every day. Physical activity can improve overall health, with a lower risk for heart disease stroke, diabetes, depression, and some cancers. (CDC) This positive variable is defined in the data source as the percentage of Americans that are considered obese.

X2: Another independent variable contributing to obesity in America is diet. The CDC confirms that obesity results from an energy imbalance. This involves too many calories and not getting enough physical activity. This positive variable is defined in the data source as the percentage of Americans that ate a combined five servings of fruits and vegetables per day. Data taken from each state’s progress towards the national Healthy People 2010 objective.

Which has a goal of 75% consuming >2 fruit servings and 50% consuming >3 vegetable servings. If people would choose fruits and vegetables over sugary snacks and fast foot they would definitely help the obesity problem in this country. According to Eric Schlosser, author of “Fast Food Nation” explains that Americans will spend over $110 billion on fast food, more than they’ll spend on movies, books, magazines, newspapers, videos, and recorded music combined. Also, one quarter of the U.S. population eats fast food every day.

DATA DESCRIPTION This report will use data taken from the Department of Health and Human Services, Centers for Disease Control and Prevention (CDC). It includes “The State Indicator Report on Physical Activity, 2010”. This report provides information on the physical activity behavior of adults within each state. To determine if an individual was physically active, respondents were asked to answer a series of questions. If they performed at least 150 minutes per week of moderate-intensity activity, or at least 75 minutes per week of vigorous-intensity activity they were considered physically active. The data that will be used is the percentage of Americans that are considered physically active.

“The State Indicator Report on Fruits and Vegetables, 2009” will be another study used which was also conducted by the CDC. This report provides for the first time information on fruit and vegetable consumption within each state. The data that will be used is the percentage of Americans that ate 5 servings of fruits and vegetables per day. Six questions were asked via telephone survey regarding the eating habits of the person being interviewed. These included questions regarding how often they drink fruit juices, how often do you eat green salad, how often do you eat carrots, and how often do you eat potatoes.

RESULTS
The predicted regression equation is, Obesity =53.40709-0.25432*Exercise-0.668* diet
Coefficient of determination or “R2” is a model that tells us about the proportion of variation explained by the regression model. It lies between 0 and 1. By including more and more variables we can increase the proportion of explained variability. From the calculation table we can see the Adjusted R2 has a value 0.570622 which implies the fitted model is in “Layman’s language” is 57% good to estimate the independent variable. So we can make the model far better by taking various considerations. A low Adj R2 implies the independent variables are not good enough to interpret the dependent variable. This may imply two things, either the independent variables chosen are not good or we should include more independent variables in the model.
The t-stat in regression checks if a particular independent variable is significant in predicting the dependent variable or not i.e. for the jth independent variable Xj we test,
H0: βj = 0 against H1: βj ≠ 0,
The test statistic,
T= βj/se(βj) ; follows a t distribution under the null hypothesis and large absolute value of T leads to rejection of the null hypothesis.
This is known as the t-test of regression.
X1(Exercise): For this independent variable the coefficient has a”-ve” value. Since X1 represents the % of population that are physically active so the obtained sign of coefficient is expected because performing exercise burns calories and lowers obesity levels. The coefficient for this variable has value -0.25432 which implies per unit increase (decrease) in exercise leads to -0.25432 unit decrease (increase) to obesity. Also the obtained p-value is 0.002943 so for any significance level more than 0.2% this variable is significant in predicting obesity.
X2(Diet): Diet denotes the % of population that ate fruit or veg. 5 times a day. Since obesity occurs because of having too much fat so it is expected that diet and obesity has an inverse relation i.e. the coefficient related to it should be “-ve” which, we are getting from the result. Again here similarly the coefficient -0.668 implies per unit decrease (increase) of diet leads to -0.668 unit increase (decrease) in dependent variable obesity. The obtained p-value is too small, almost 0 so by the same argument the variable “Diet” is significant.
Since both variables have small p-values they both are significant. “Diet” has a smaller p-value thus it is more significant than the other variable “Exercise”.
Multicollinearity is the occurrence of several independent variables in a multiple regression model that are closely correlated to one another. Multicollinearity can cause strange results when attempting to study how well individual independent variables contribute to an understanding of the dependent variable. In general, multicollinearity can cause wide confidence intervals and strange p-value for independent variables.
The obtained correlation matrix is, | Exercise | Diet | Exercise | 1 | | Diet | 0.560055 | 1 |

So we can see there is a positive moderate correlation is present between the independent variables Exercise and Diet.

Works Cited

National Center for Chronic Disease Prevention and Health Promotion. (2009). Physical Activity. Retrieved November 2, 2012 from http://www.cdc.gov/physicalactivity/index.html

National Center for Chronic Disease Prevention and Health Promotion. (2009). State Indicator Report on Fruits and Vegetables, 2009. Retrieved November 2, 2012 from http://www.cdc.gov/nutrition/downloads/StateIndicatorReport2009.pdf

Schlosser, E. (2001). Fast food nation: The dark side of the all-American meal. Boston: Houghton Mifflin.

Warner, Jennifer (2004) . Video Games, TV Double Childhood Obesity Risk. Retrieved November
2, 2012 from http://children.webmd.com/news/20040702/video-games-tv-double-childhood-obesity-risk

Appendix | Dependend Variable "Obesity" | | Independent Variable #1 (X1) "Exercise" | | Independent Variable #2 "Diet" | | | | | | | US STATE | % of Population overweight | | % of Population that are physically active | | % of Population that ate fruits and veg. 5x a day | | | | | | | ALABAMA | 33 | | 59 | | 9.8 | ALASKA | 26.2 | | 72.5 | | 13.9 | ARIZONA | 24.7 | | 66.5 | | 16.1 | ARKANSAS | 30.9 | | 62.1 | | 11.2 | CALIFORNIA | 24.7 | | 66.7 | | 16.1 | CONNECTICUT | 23 | | 68.3 | | 16.2 | COLORADO | 21.4 | | 70.9 | | 15.2 | DELAWARE | 28.7 | | 64.3 | | 12.3 | DIST OF COLUMBIA | 22.7 | | 66.3 | | 20.1 | FLORIDA | 27.2 | | 63.5 | | 15.6 | GEORGIA | 30.4 | | 66.8 | | 13.3 | HAWAII | 23.1 | | 70.4 | | 17.5 | IDAHO | 26.9 | | 70.7 | | 13 | ILLINOIS | 28.7 | | 63.1 | | 13.7 | INDIANA | 30.2 | | 64.3 | | 13.5 | IOWA | 29.1 | | 64.1 | | 12.3 | KANSAS | 30.1 | | 64.7 | | 10.6 | KENTUCKY | 31.8 | | 57.9 | | 10.8 | LOUISIANA | 31.7 | | 56 | | 11.5 | MAINE | 27.4 | | 71.3 | | 17.7 | MARYLAND | 27.9 | | 64.9 | | 15.4 | MASS | 23.6 | | 66.9 | | 16.4 | MICHIGAN | 31.7 | | 66.6 | | 11.8 | MINNESOTA | 25.4 | | 62.9 | | 11.6 | MISSISSIPPI | 34.5 | | 57.2 | | 8.8 | MISSOURI | 31.4 | | 65 | | 11.2 | MONTANA | 23.5 | | 72.4 | | 14.5 | NEBRASKA | 27.9 | | 67.5 | | 14 | NEVADA | 23.1 | | 62.5 | | 11.8 | NEW HAMPSHIRE | 25.5 | | 70 | | 16.2 | NEW JERSEY | 24.8 | | 62.9 | | 14.9 | NEW MEXICO | 25.6 | | 67.9 | | 12.5 | NEW YORK | 24.5 | | 63.9 | | 16.5 | NORTH CAROLINA | 28.6 | | 60.9 | | 10.8 | NORTH DAKOTA | 27.9 | | 67.3 | | 13.3 | OHIO | 29.7 | | 65.6 | | 12.2 | OKLAHOMA | 31.3 | | 62.6 | | 9.3 | OREGON | 27.6 | | 70.6 | | 15.6 | PENNSYLVANIA | 29.2 | | 66.1 | | 15.1 | RHODE ISAND | 26 | | 66.3 | | 14.6 | SOUTH CAROLINA | 32 | | 64.3 | | 9.3 | SOUTH DAKOTA | 27.7 | | 62.7 | | 10.1 | TENNESSEE | 31.7 | | 51.8 | | 13.1 | TEXAS | 31.7 | | 63.7 | | 14.3 | UTAH | 23 | | 71.8 | | 13.2 | VERMONT | 23.9 | | 73.3 | | 17.9 | VIRGINIA | 26.4 | | 67.1 | | 14.2 | WASHINGTON | 26.2 | | 69.6 | | 15.1 | WEST VIRGINIA | 32.9 | | 62.2 | | 10.3 | WISCONSIN | 26.9 | | 70.7 | | 13.7 | WYOMING | 25.7 | | 70.9 | | 14.6 |

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